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Found 16 papers, 10 papers with code
Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables
Different from previous fact extraction and verification tasks that only consider evidence of a single format, FEVEROUS brings further challenges by extending the evidence format to both plain text and tables.
Teaching Text-to-Image Models to Communicate in Dialog
In this paper, we focus on the innovative dialog-to-image generation task, where the model synthesizes a high-resolution image aligned with the given dialog context as a response.
Improving the Generalization Ability in Essay Coherence Evaluation through Monotonic Constraints
To address these concerns, we propose a coherence scoring model consisting of a regression model with two feature extractors: a local coherence discriminative model and a punctuation correction model.
How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension
We further analyze how well different paradigms of current multi-answer MRC models deal with different types of multi-answer instances.
Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension
We convert KB subgraphs into passages to narrow the gap between KB schemas and questions, which enables our model to benefit from recent advances in multilingual pre-trained language models (MPLMs) and cross-lingual machine reading comprehension (xMRC).
Cross-Lingual Question Answering
Machine Reading Comprehension
Event Transition Planning for Open-ended Text Generation
Open-ended text generation tasks, such as dialogue generation and story completion, require models to generate a coherent continuation given limited preceding context.
Extract, Integrate, Compete: Towards Verification Style Reading Comprehension
In this paper, we present a new verification style reading comprehension dataset named VGaokao from Chinese Language tests of Gaokao.
Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction
Document-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences.
Ranked #48 on
Relation Extraction
on DocRED
Why Machine Reading Comprehension Models Learn Shortcuts?
A thorough empirical analysis shows that MRC models tend to learn shortcut questions earlier than challenging questions, and the high proportions of shortcut questions in training sets hinder models from exploring the sophisticated reasoning skills in the later stage of training.
Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models
Further analysis shows that Lattice-BERT can harness the lattice structures, and the improvement comes from the exploration of redundant information and multi-granularity representations.
Domain Adaptation for Semantic Parsing
In this paper, we propose a novel semantic parser for domain adaptation, where we have much fewer annotated data in the target domain compared to the source domain.
Integrating Relation Constraints with Neural Relation Extractors
However, such models often make predictions for each entity pair individually, thus often fail to solve the inconsistency among different predictions, which can be characterized by discrete relation constraints.
A Sketch-Based System for Semantic Parsing
The sketch is a high-level structure of the logical form exclusive of low-level details such as entities and predicates.
Enhancing Key-Value Memory Neural Networks for Knowledge Based Question Answering
However, extending KV-MemNNs to Knowledge Based Question Answering (KB-QA) is not trivia, which should properly decompose a complex question into a sequence of queries against the memory, and update the query representations to support multi-hop reasoning over the memory.
Lattice CNNs for Matching Based Chinese Question Answering
Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages like Chinese where there is no natural space to segment words explicitly.
Modeling discourse cohesion for discourse parsing via memory network
Identifying long-span dependencies between discourse units is crucial to improve discourse parsing performance.
